Project observations with a fitted CUDA-aware PCA model

predict.cuda_pca() applies the fitted centring, scaling, and loadings to new observations. Named features may be supplied in any order and are aligned safely before projection. If the fitted model has feature names, unnamed or mismatched columns are rejected instead of being used in the wrong order.

Usage

# S3 method for class 'cuda_pca'
predict(object, newdata, device = c("model", "auto", "cuda", "cpu"), ...)

Arguments

object

A fitted cuda_pca object.

newdata

A finite numeric matrix or data frame with observations in rows and the model features in columns. When omitted, the training scores in object$x are returned.

device

Where to compute the projection. "model" reuses the actual device of the fitted model; "auto", "cuda", and "cpu" follow the usual cudaverse device-selection rules.

...

Must be empty.

Value

A numeric matrix of component scores. New observation names and stable component names are retained. A recomputed prediction includes stage-level provenance and is materialized as an R matrix on the CPU. The native backend also retains shared device storage so a subsequent native distance or kNN operation can reuse the scores without uploading them. Omitting newdata returns the validated stored training scores unchanged; that retrieval does not create a prediction stage.

See also

cuda_pca()

Examples

train <- as.matrix(iris[1:100, 1:4])
fit <- cuda_pca(train, n_components = 2, device = "cpu")
predict(fit, as.matrix(iris[101:105, 1:4]), device = "cpu")
#>          PC1        PC2
#> 101 3.532286 -0.3768000
#> 102 2.491451  0.3064927
#> 103 3.622220 -0.6979323
#> 104 3.020128 -0.1303527
#> 105 3.374626 -0.3093205
#> attr(,"device")
#> [1] "cpu"
#> attr(,"provenance_schema")
#> [1] "cudaverse-stage/1"
#> attr(,"requested_device")
#> [1] "cpu"
#> attr(,"compute_device")
#> [1] "cpu"
#> attr(,"compute_stages")
#> attr(,"compute_stages")$projection
#> $requested_device
#> [1] "cpu"
#> 
#> $device
#> [1] "cpu"
#> 
#> $backend
#> [1] "base"
#> 
#> $selection_reason
#> [1] "explicit_cpu"
#> 
#> $fallback
#> [1] FALSE
#> 
#> $output_device
#> [1] "cpu"
#> 
#> attr(,"class")
#> [1] "cuda_stage"
#> 
#> attr(,"backend")
#> [1] "base"
#> attr(,"parameters")
#> attr(,"parameters")$n_components
#> [1] 2
#> 
#> attr(,"source_device")
#> [1] "cpu"
#> attr(,"source_class")
#> [1] "matrix"